Recommender systems seek to predict the ‘rating’ or the ‘preference’ that a user would give to an item. Two basic entities that are included in any recommender system are, on one hand, items like books, people to contact, songs, articles, merchandise, etc. and, on the other hand, users. Recommender systems typically need some input information on these entities such as demographic data of the users, item features, and users' ratings of the items. The ratings can be explicit or implicit, binary or on different scales.
For recommendations to work effectively, it is usually helpful that the recommender system has some knowledge about the user pertaining to their interests and preferences. Further, the recommender system should successfully link the available information about the user to the items on offer to make a recommendation. Finally, the user should understand or have some other basis for trusting the recommendations being made to him. A recommendation problem is to make a prediction of a user's ratings of unrated items and to recommend one or more items of relevance to the user.
At least six types of computer-supported recommender systems can be identified:
1) Content-based systems recommend items that are similar to the items the focal user liked in the past.
2) Collaborative filtering systems recommend items to a particular user based on the ratings of users who show similar tastes for the same items.
3) Item-based collaborative filtering or item-by-item collaborative filtering.
4) Demographic filtering categorizes users according to demographic data and generates recommendations based on demographic classes.
5) Average ratings are non-personalized recommendations based on what other users have said about the items on average.
6) Recommendation support systems are systems that do not automate the recommendation process but support people in sharing recommendations. These are typically non-personalized recommendations.
The first major problem in conventional recommender systems is the cold start problem and the long tail problem. These systems can not recommend items not yet rated. Many items in catalogs are found to have insufficient or inadequate ratings so they are either rarely recommended or never recommended to users.
The second major problem of recommender systems is the lack of novelty and serendipity. Content-based filtering recommends similar items, which usually means that it does not provide any radical change in the types of items recommended to users, while collaborative filtering generally recommends very popular items to anyone.
The start-up or new user problem is considered to be the third major problem of recommender systems. New users of a system have few or no ratings of items that can be used to determine similar items or similar users needed to provide recommendations in content-based and collaborative filtering systems.
The recommendation system enabled by embodiments are designed to address the above-mentioned problems of cold start, lack of novelty and new users.